Statistics for Data Science Practice Exam
Statistics for Data Science
Those possibilities in Data Science are known as Statistical Analysis. Most of us can't help thinking about how could data in the form of text, images, videos, and other profoundly unstructured formats get easily processed by Machine Learning models. Statistics acts as a pathway to understand your data and process that for successful results.
Skills Required
• Python programming.
• R programming.
• Hadoop platform.
• SQL databases.
• Machine learning and AI.
• Data visualization.
• Business strategy.
Career Opportunity
• Data Scientist.
• Machine Learning Engineer.
• Machine Learning Scientist.
• Applications Architect.
• Enterprise Architect.
• Data Architect.
• Infrastructure Architect.
• Data Engineer.
Table of Contents
- Introduction
- Sample or population data?
- The fundamentals of descriptive statistics
- Measures of central tendency, asymmetry, and variability
- Practical example: descriptive statistics
- Distributions
- Estimators and estimates
- Confidence intervals: advanced topics
- Practical example: inferential statistics
- Hypothesis testing: Introduction
- Hypothesis testing: Let's start testing!
- Practical example: hypothesis testing
- The fundamentals of regression analysis
- Subtleties of regression analysis
- Assumptions for linear regression analysis
- Dealing with categorical data
- Practical example: regression analysis
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